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Volumn , Issue , 2006, Pages 1141-1145

Temporal data mining in dynamic feature spaces

Author keywords

[No Author keywords available]

Indexed keywords

CONCEPT DRIFT; DATA STREAMS; FEATURE SPACES; TEMPORAL DATA MINING;

EID: 84866613435     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2006.157     Document Type: Conference Paper
Times cited : (27)

References (20)
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    • Fan, W.1
  • 6
    • 0034499376 scopus 로고    scopus 로고
    • A note on the utility of incremental learning
    • C. Giraud-Carrier. A note on the utility of incremental learning. AI Communications, 13(4):215-223, 2000.
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    • Giraud-Carrier, C.1
  • 9
    • 33646504407 scopus 로고    scopus 로고
    • On the utility of incremental feature selection for the classification of textual data streams
    • Proc. of the 10th Panhellenic Conf. on Informatics
    • I. Katakis, G. Tsoumakas, and I. Vlahavas. On the utility of incremental feature selection for the classification of textual data streams. In Proc. of the 10th Panhellenic Conf. on Informatics, LNCS 3746, pages 338-348, 2005.
    • (2005) LNCS , vol.3746 , pp. 338-348
    • Katakis, I.1    Tsoumakas, G.2    Vlahavas, I.3
  • 10
    • 84883713774 scopus 로고    scopus 로고
    • Learning drifting concepts: Example selection vs. example weighting
    • R. Klinkenberg. Learning drifting concepts: Example selection vs. example weighting. Intelligent Data Analysis, 8(3):281-300, 2004.
    • (2004) Intelligent Data Analysis , vol.8 , Issue.3 , pp. 281-300
    • Klinkenberg, R.1
  • 12
    • 78149292125 scopus 로고    scopus 로고
    • Dynamic weighted majority: A new ensemble method for tracking concept drift
    • J. Z. Kolter and M. A. Maloof. Dynamic weighted majority: A new ensemble method for tracking concept drift. In Proc. of the 3rd IEEE Intl. Conf. on Data Mining, pages 123-130, 2003.
    • (2003) Proc. of the 3rd IEEE Intl. Conf. on Data Mining , pp. 123-130
    • Kolter, J.Z.1    Maloof, M.A.2
  • 14
    • 0010012318 scopus 로고
    • Incremental learning from noisy data
    • J. C. Schlimmer and R. H. Granger, Jr. Incremental learning from noisy data. Machine Learning, 1(3):317-354, 1986.
    • (1986) Machine Learning , vol.1 , Issue.3 , pp. 317-354
    • Schlimmer, J.C.1    Granger Jr., R.H.2
  • 15
    • 22544451786 scopus 로고    scopus 로고
    • Learning concept drift with a committee of decision trees
    • Technical report, Department of Computer Sciences, University of Texas at Austin, September
    • K.O.Stanley. Learning concept drift with a committee of decision trees. Technical report, Department of Computer Sciences, University of Texas at Austin, September 2003.
    • (2003)
    • Stanley, K.O.1
  • 16
    • 26444562687 scopus 로고    scopus 로고
    • The problem of concept drift: Definitions and related work
    • Technical report, Department of Computer Science Trinity College Dublin, Ireland, April
    • A. Tsymbal. The problem of concept drift: Definitions and related work. Technical report, Department of Computer Science Trinity College Dublin, Ireland, April 2004.
    • (2004)
    • Tsymbal, A.1
  • 17
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    • Incremental induction of decision trees
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    • (1989) Machine Learning , vol.4 , pp. 161-186
    • Utgoff, P.E.1
  • 19
    • 0030126609 scopus 로고    scopus 로고
    • Learning in the presence of concept drift and hidden contexts
    • G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine Learning, 23:69-101, 1996.
    • (1996) Machine Learning , vol.23 , pp. 69-101
    • Widmer, G.1    Kubat, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.